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At Microsoft Research AI for Science, we believe machine learning and artificial intelligence has the potential to transform scientific modelling and discovery crucial for solving the most pressing problems facing society including sustainable materials and discovery of new drugs.

We seek a highly motivated Senior Applied Researcher to join our Biomolecular Emulator (BioEmu) team. The BioEmu project aims to model the dynamics and function of proteins - how they change shape, bind to each other, and bind small molecules. This approach will help us to understand biological function and dysfunction on a structural level and lead to more effective and targeted drug discovery. Our BioEmu-1 model was published in Science (see our blog post for links to our open-source software and other resources and this explainer video).

Required:

• PhD or equivalent research experience in Chemistry, Biophysics, Physics, Computer Science, Bioinformatics or a related field.
• Demonstrated passion and proven excellence in computational molecular biology, biophysics, or bioinformatics via impactful projects, publications, or open‑source code.
• Experience with biomolecular modeling/bioinformatics (e.g., folding systems, structural analysis, MD simulation, structure/genome databases).
• Excellent technical communication for interdisciplinary work.
• Comfortable with real‑world data that lack structure/cleanliness/completeness.
• Proficiency in Python and data analysis packages (NumPy, SciPy, Pandas).

Preferred:

• Experience with deep learning methods and software packages (PyTorch, JAX, Tensorflow).
  
• Experience with structural biology or molecular biology data/techniques (e.g., cryo‑EM, binding assays, spectroscopy, expression, sequencing).
  
• Background or advanced training in structural biology; familiarity with structure prediction and/or MD work-flows.
  

#Research #AI for Science

• Model biomolecular structure and dynamics at scale: Use BioEmu, structure/sequence databases, and complementary tools to analyze proteins and complexes.
• Innovate and improve analysis technologies: Develop and implement scalable technologies to analyze and quantify conformational transitions, structural flexibility, and changes in complex formation for many biomolecular complexes.
• Generate biological hypotheses and insights: Integrate biomolecular structure and dynamics data to generate experimentally-testable predictions and biological insights.
• Collaborate with experimentalists: collaborate with wetlab scientists to test computational predictions. Learn to read and interpret their data and work together to challenge and improve hypothesis of biomolecular function.
• Support drug‑targeting strategies: Partner with leading drug discovery experts to identify targets and propose mechanisms of action.
• Operationalize at scale: Build robust Python pipelines; automate large‑scale inference and analysis jobs on Azure with reproducible work-flows.
• Partner across disciplines: communicate clearly with ML researchers and experimental/computational biologists; present results and influence project direction.
• Work autonomously and as a team player, regularly reporting insights, risks, and next steps.
• Aim for impact: translate findings into artifacts others can use (code, datasets, internal reports, and when appropriate - papers).